Anomalous Motion Detection on Highway Using Deep Learning

Harpreet Singh; Emily M. Hand; Kostas Alexis

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arXiv · 2006.08143

Anomalous Motion Detection on Highway Using Deep Learning

Harpreet Singh·Emily M. Hand·Kostas Alexis

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Abstract

Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no dataset exists in this type of environment. This paper presents a new anomaly detection dataset - the Highway Traffic Anomaly (HTA) dataset - for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods. Our results show that state-of-the-art models built for settings with a stationary camera do not translate well to a more dynamic environment. The proposed variations to these SoTA methods show promising results on the new HTA dataset.

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BibTeX RIS

Harpreet Singh, Emily M. Hand, Kostas Alexis. 2020-06-15. Anomalous Motion Detection on Highway Using Deep Learning. https://arxiv.org/abs/2006.08143

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